Recent advancements in Internet technologies have led to the proliferation of online shopping portals, offering customers the convenience of purchasing a diverse array of products from the comfort of their homes. However, navigating through these online portals can be time-consuming, as they often feature numerous similar products with varying price tags and brands. Analyzing customer comments manually to pinpoint the top products can consumes significant amount of time. Automated recommendation systems have emerged as a valuable solution to help customers make informed online purchases that align with their preferences and needs. These systems leverage technology to assist users in finding the best-branded products in an efficient manner. The continuous innovation and development in Information Technology have led to the proliferation of numerous E-commerce websites, motivating customers to explore and purchase products online without the need to visit physical stores. However, in E-commerce portals, customers are often required to manually sift through the vast product offerings to identify the best-branded products. The paper proposed a novel automated recommender system that leverages web crawling and scraping methods to find the best deals on E-commerce websites. The scraping dynamically scan HTML elements/tags and process queries without storing information in a local database, thereby improving storage efficiency and processing power.